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A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption

Author

Listed:
  • Rasika B. Naik

    (Sir Padampat Singhania University)

  • Udayprakash Singh

    (Sir Padampat Singhania University)

Abstract

Because of the COVID-19 pandemic, most of the tasks have shifted to an online platform. Sectors such as e-commerce, sensitive multi-media transfer, online banking have skyrocketed. Because of this, there is an urgent need to develop highly secure algorithms which can not be hacked into by unauthorized users. The method which is the backbone for building encryption algorithms is the pseudo-random number generator based on chaotic maps. Chaotic maps are mathematical functions that generate a highly arbitrary pattern based on the initial seed value. This manuscript gives a summary of how the chaotic maps are used to generate pseudo-random numbers and perform multimedia encryption. After carefully analyzing all the recent literature, we found that the lowest correlation coefficient was 0.00006, which was achieved by Ikeda chaotic map. The highest entropy was 7.999995 bits per byte using the quantum chaotic map. The lowest execution time observed was 0.23 seconds with the Zaslavsky chaotic map and the highest data rate was 15.367 Mbits per second using a hyperchaotic map. Chaotic map-based pseudo-random number generation can be utilized in multi-media encryption, video-game animations, digital marketing, chaotic system simulation, chaotic missile systems, and other applications.

Suggested Citation

  • Rasika B. Naik & Udayprakash Singh, 2024. "A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption," Annals of Data Science, Springer, vol. 11(1), pages 25-50, February.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:1:d:10.1007_s40745-021-00364-7
    DOI: 10.1007/s40745-021-00364-7
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    References listed on IDEAS

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    1. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Xuan Huang & Lingfeng Liu & Xiangjun Li & Minrong Yu & Zijie Wu, 2019. "A New Pseudorandom Bit Generator Based on Mixing Three-Dimensional Chen Chaotic System with a Chaotic Tactics," Complexity, Hindawi, vol. 2019, pages 1-9, June.
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